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Optimization & Theory

43,292 papers (3 classified directly here)

Subtopics · a paper can sit in several, so these overlap

Optimization (17,731) Learning Theory (6,809) Bayesian Inference (6,267) Theory (5,579) Statistical Learning (4,564) Neural Network Optimization (4,155) Stochastic Processes (2,948) Stochastic Methods (1,283) Loss Functions (1,220) Distributed Learning (1,202) Online Algorithms (1,013) Statistics (822) Generalization (376) Information Theory (370) Sample Complexity (282) Sparse Optimization (241) Game Theory (151) Convex Optimization (132) Optimal Transport (104) Robust Optimization (84) Distributed Optimization (73) Approximation Algorithms (33)

Papers per year

1
52
352
828
3,646
5,854
2000
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Papers (including subtopics)

Using 3D Topological Connectivity for Ghost Particle Reduction in Flow Reconstruction CVPR 2022 Using Deep Mixture-of-Experts to Detect Word Meaning Shift for TempoWiC EMNLP 2022 Using MaxSAT for Efficient Explanations of Tree Ensembles AAAI 2022 Using Mixed-Effects Models to Learn Bayesian Networks from Related Data Sets PGM 2022 Using Partial Monotonicity in Submodular Maximization NeurIPS 2022 Using Sampling to Estimate and Improve Performance of Automated Scoring Systems with Guarantees AAAI 2022 Using Shapley Values and Variational Autoencoders to Explain Predictive Models with Dependent Mixed Features JMLR 2022 Using time-series privileged information for provably efficient learning of prediction models AISTATS 2022 Utility Theory for Sequential Decision Making ICML 2022 Validating Causal Inference Methods ICML 2022 Value Function Approximations via Kernel Embeddings for No-Regret Reinforcement Learning ACML 2022 Value Function based Difference-of-Convex Algorithm for Bilevel Hyperparameter Selection Problems ICML 2022 ValueNetQP: Learned One-step Optimal Control for Legged Locomotion L4DC 2022 Vanishing Curvature in Randomly Initialized Deep ReLU Networks AISTATS 2022 Variance Minimization in the Wasserstein Space for Invariant Causal Prediction AISTATS 2022 Variance Reduced EXTRA and DIGing and Their Optimal Acceleration for Strongly Convex Decentralized Optimization JMLR 2022 Variance Reduced ProxSkip: Algorithm, Theory and Application to Federated Learning NeurIPS 2022 Variational Autoencoders: A Harmonic Perspective AISTATS 2022 Variational autoencoders in the presence of low-dimensional data: landscape and implicit bias ICLR 2022 Variational Continual Proxy-Anchor for Deep Metric Learning AISTATS 2022 Variational Gaussian Processes: A Functional Analysis View AISTATS 2022 Variational Inference for Discriminative Learning with Generative Modeling of Feature Incompletion ICLR 2022 Variational Inference for Infinitely Deep Neural Networks ICML 2022 Variational Inference in high-dimensional linear regression JMLR 2022 Variational Inference MPC using Normalizing Flows and Out-of-Distribution Projection RSS 2022